# Student-AI Interaction: A Case Study of CS1 students

arXiv: https://arxiv.org/abs/2407.00305 · HTML: https://mlsanity.com/paper/2407.00305/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2024.

## Built on by (most cited first, 12 of 12)

- [From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions](https://mlsanity.com/paper/2504.18691/) (2025)
- [Prompt Programming: A Platform for Dialogue-based Computational Problem Solving with Generative AI Models](https://mlsanity.com/paper/2503.04267/) (2025)
- [Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education](https://mlsanity.com/paper/2506.19107/) (2025)
- [Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Education](https://mlsanity.com/paper/2512.23982/) (2025)
- [Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming](https://mlsanity.com/paper/2604.27134/) (2026)
- [Evaluating the Effectiveness of Large Language Models in Solving Simple Programming Tasks: A User-Centered Study](https://mlsanity.com/paper/2507.04043/) (2025)
- [To Google or To ChatGPT? A Comparison of CS2 Students' Information Gathering Approaches and Outcomes](https://mlsanity.com/paper/2501.11935/) (2025)
- [Investigating Student Interaction Patterns with Large Language Model-Powered Course Assistants in Computer Science Courses](https://mlsanity.com/paper/2509.08862/) (2025)
- [GenAI Voice Mode in Programming Education](https://mlsanity.com/paper/2509.10596/) (2025)
- [AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective](https://mlsanity.com/paper/2606.22809/) (2026)
- [Analysis of Types of Inquiries in Student-AI Interaction: A case study of two CS2 tasks](https://mlsanity.com/paper/2608.17919/) (2026)
- [Understanding Generative AI Use in Programming MOOCs: The Role of Course Context and Learner Characteristics](https://mlsanity.com/paper/2610.04447/) (2026)

## Abstract (arXiv)

The new capabilities of generative artificial intelligence tools Generative AI, such as ChatGPT, allow users to interact with the system in intuitive ways, such as simple conversations, and receive (mostly) good-quality answers. These systems can support students' learning objectives by providing accessible explanations and examples even with vague queries. At the same time, they can encourage undesired help-seeking behaviors by providing solutions to the students' homework. Therefore, it is important to better understand how students approach such tools and the potential issues such approaches might present for the learners. In this paper, we present a case study for understanding student-AI collaboration to solve programming tasks in the CS1 introductory programming course. To this end, we recruited a gender-balanced majority non-white set of 15 CS1 students at a large public university in the US. We observed them solving programming tasks. We used a mixed-method approach to study their interactions as they tackled Python programming tasks, focusing on when and why they used ChatGPT for problem-solving. We analyze and classify the questions submitted by the 15 participants to ChatGPT. Additionally, we analyzed user interaction patterns, their reactions to ChatGPT's responses, and the potential impacts of Generative AI on their perception of self-efficacy. Our results suggest that in about a third of the cases, the student attempted to complete the task by submitting the full description of the tasks to ChatGPT without making any effort on their own. We also observed that few students verified their solutions. We discuss the results and their potential implications.